Canaries in the column? AI exposure and the UK’s hiring slowdown – Bank Underground

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BankUnderground

Artificial intelligence, Macroeconomics

06/08/202606/08/2026

6 Minutes

Haley Schlicht

From Silicon Valley executives promising to automate white-collar work to headlines claiming AI is foreclosing the graduate entry market, the strained ‘low fire, low hire’ environment has increasingly been ascribed to technological transformation. UK vacancies nearly halved since their 2022 peak – a contraction so sustained it has reshaped the British hiring market for the better part of three years. This post examines how evidence of AI-driven transformation at the hiring margin is proving considerably more tenuous than the headlines suggest.

Brynjolfsson et al (2025) at Stanford University champion vacancy compression in AI-exposed occupations as a canary, the labour market’s early warning system and a leading indicator of technological displacement in adolescence. Lambert and Schindler (2026) counter with a discomfiting reframe. Rather than AI, the hiring market is shifting due to the structural upheaval that remote working visited upon firms’ internal labour dynamics, breaking the lower rungs of the career ladder.

The UK makes for an exigent test case. The vacancy retrenchment here has been severe even compared to peer economies , compounded by the previous post-pandemic over-hiring, an energy shock from a land war in Europe, cyclical deterioration, and additional pressures on labour demand. This post builds off other monitoring efforts and this broader framework for tracking how AI may diffuse through the economy. This article focuses on the labour-demand channel within that framework, seeking to disentangle AI’s contribution from the surrounding storms to reveal whether the hiring market is beginning to display the kinds of patterns we might expect during the early stages of a general-purpose technology transition. What looks like weather damage to the labour market may, beneath the surface, already be a shifted shoreline.

The vacancy retreat

The fall in vacancies was not spawned from a single event. The labour market, which was exceptionally tight when vacancies peaked in 2022, gradually loosened in the following years as the cycle unwound. Pandemic over-hire met its correction as firms confronted the scale of labour they had banked against demand that never fully materialised. Remote working may have played a role too, raising the cost of training junior staff and prompting firms to withhold intake . National Living Wage upratings and changes to employer National Insurance contributions formed part of the wider labour-demand environment. Simultaneously, businesses weighing AI investment may have become more reluctant to refill headcount .

Occupational signals

Identifying technology’s footprint in the UK labour market first requires a credible measure of where AI capabilities augment labour. To address divergence between individual measures , this analysis coalesces five leading indices from the academic and industry literature, each capturing a different dimension of occupational exposure. Sourcing the original works’ task-level ‘AI susceptibility’ assessments, we reconstruct the AI exposure scores using UK occupational and industry employment data aggregated with pre-treatment employment weights. As such, the exposure scores are constructed to reflect the structure of the British labour market rather than a US-derived benchmark. By benchmarking multiple exposure frameworks and validating the resulting scores against reported AI adoption in the Bank’s Decision Maker Panel and ONS Business Insights and Conditions Survey, the measure aims to provide a more robust signal of technological exposure than any single index alone.

Chart 1 shows the strongest signal of technology disruption to hiring appears at the occupational level where occupation-indexed AI exposure exhibits a strong, monotonic correlation with online-vacancy contraction across UK occupations.

Chart 1: Growth in advertised vacancies falls as occupational AI exposure rises

Notes: Spearman p = -0.70, p

Sorted into terciles by exposure percentile in Chart 2, high-exposure groups lost 15% of online adverts, mid-exposure 10%, low-exposure 6%. The sharpest declines land where the task-based account predicts; customer service down 23%, administrative occupations down 22%. These are the task bundles – scheduling, correspondence, routine information processing – that generative systems can now credibly substitute.

Chart 2: Growth of online job adverts fell most in high-exposure occupations

Notes: Pre-period uses valid 2019 year-on-year observations because OJA starts in January 2018. Post-period covers available observations in 2023–26 Q1; March 2026 occupation cells are suppressed in the source. Terciles reflect SOC two-digit sub-major groups grouped by composite Al exposure score.

Sources: ONS Online Job Adverts via...